Advances in Optimization and Machine Learning in Indoor Environmental Quality and Energy in Buildings
A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".
Deadline for manuscript submissions: 30 June 2024 | Viewed by 3030
Special Issue Editors
Interests: optimization; mathematical programming; machine learning; algorithms; decision support systems
Interests: energy efficient buildings; HVAC systems; solar thermal energy; indoor environmental quality; ventilation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue aims at including works applying optimization and machine learning techniques in assessing indoor environmental quality and/or its relationship with building energy systems. The abovementioned techniques, coupled with Internet of Things sensors, can be utilized in real-time monitoring of various aspects related to indoor environment and energy in buildings, thus allowing for intelligent self-learning procedures towards design and operation optimization. Within this context, works investigating the development and application of optimization and machine learning techniques on indoor environmental quality and/or energy systems in buildings are of interest.
Techniques of interest include classical mathematical programming methods, AI-based techniques, deep learning models, surrogate models, derivative-free methods, etc.
Topics of interest include indoor environmental quality components, indoor environment sensors, ventilation, HVAC systems, building energy systems, energy performance of buildings, etc.
Dr. Nikolaos Ploskas
Dr. Giorgos Panaras
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- indoor environmental quality
- energy in buildings
- optimization
- machine learning
- indoor air quality
- thermal comfort
- ventilation
- HVAC systems
- IoT sensors
- intelligent systems
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: A multi-output machine learning approach for predicting building end-use energy consumption in various space types
Authors: Zeynep Duygu Tekler
Affiliation: National University of Singapore, Singapore
Title: Data-driven methodologies for predicting the energy consumption of buildings
Authors: Giouli Michalakakou
Affiliation: University of Patras, Greece
Title: Title Pending
Authors: Efrosini Giama
Affiliation: Aristotle University of Thessaloniki, Greece
Title: Computer vision and machine learning for real-time occupancy detection: A pathway to enhanced building systems control in complex and high occupancy environments
Authors: John Kaiser Calautit
Affiliation: University of Nottingham, UK